Recent techniques for acquiring geometry and reflectance of objects have enabled richer and more realistic renderings in computer graphics. Many of these techniques use a combination of 3D scanning and photography under different lighting conditions to acquire 3D models of the object's shape and how it reflects light. When both of these characteristics are measured, the models can be used to faithfully render how the object would look from any angle, reflecting the light of any environment.
Some of the current reflectance acquisition techniques photograph an object under a small number of lighting conditions, and use this limited reflectance information to infer BRDFs (bidirectional reflectance distribution functions) across the entire object surface. However, such techniques miss some of the spatially-varying effects of specular reflections. Other techniques acquire object reflectance using many images of the object under a dense sampling of lighting directions. These techniques model spatially-varying BRDFs more accurately but are significantly more data intensive, complicating the capture of live objects.
There is a need for methods and systems that allow surface normal maps and reflectance information to be acquired from an object while overcoming these and other shortcomings.